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Teng Y, Chen C, Shu X, Zhao F, Zhang L, Xu J. Automated, fast, robust brain extraction on contrast-enhanced T1-weighted MRI in presence of brain tumors: an optimized model based on multi-center datasets. Eur Radiol 2024; 34:1190-1199. [PMID: 37615767 PMCID: PMC10853304 DOI: 10.1007/s00330-023-10078-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 07/12/2023] [Accepted: 07/14/2023] [Indexed: 08/25/2023]
Abstract
OBJECTIVES Existing brain extraction models should be further optimized to provide more information for oncological analysis. We aimed to develop an nnU-Net-based deep learning model for automated brain extraction on contrast-enhanced T1-weighted (T1CE) images in presence of brain tumors. METHODS This is a multi-center, retrospective study involving 920 patients. A total of 720 cases with four types of intracranial tumors from private institutions were collected and set as the training group and the internal test group. Mann-Whitney U test (U test) was used to investigate if the model performance was associated with pathological types and tumor characteristics. Then, the generalization of model was independently tested on public datasets consisting of 100 glioma and 100 vestibular schwannoma cases. RESULTS In the internal test, the model achieved promising performance with median Dice similarity coefficient (DSC) of 0.989 (interquartile range (IQR), 0.988-0.991), and Hausdorff distance (HD) of 6.403 mm (IQR, 5.099-8.426 mm). U test suggested a slightly descending performance in meningioma and vestibular schwannoma group. The results of U test also suggested that there was a significant difference in peritumoral edema group, with median DSC of 0.990 (IQR, 0.989-0.991, p = 0.002), and median HD of 5.916 mm (IQR, 5.000-8.000 mm, p = 0.049). In the external test, our model also showed to be robust performance, with median DSC of 0.991 (IQR, 0.983-0.998) and HD of 8.972 mm (IQR, 6.164-13.710 mm). CONCLUSIONS For automated processing of MRI neuroimaging data presence of brain tumors, the proposed model can perform brain extraction including important superficial structures for oncological analysis. CLINICAL RELEVANCE STATEMENT The proposed model serves as a radiological tool for image preprocessing in tumor cases, focusing on superficial brain structures, which could streamline the workflow and enhance the efficiency of subsequent radiological assessments. KEY POINTS • The nnU-Net-based model is capable of segmenting significant superficial structures in brain extraction. • The proposed model showed feasible performance, regardless of pathological types or tumor characteristics. • The model showed generalization in the public datasets.
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Affiliation(s)
- Yuen Teng
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Chaoyue Chen
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
- West China Hospital, No. 37, GuoXue Alley, Chengdu, 610041, People's Republic of China.
| | - Xin Shu
- College of Computer Science, Sichuan University, Chengdu, People's Republic of China
| | - Fumin Zhao
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Lei Zhang
- College of Computer Science, Sichuan University, Chengdu, People's Republic of China.
| | - Jianguo Xu
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China.
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
- West China Hospital, No. 37, GuoXue Alley, Chengdu, 610041, People's Republic of China.
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Brighi C, Waddington DEJ, Keall PJ, Booth J, O’Brien K, Silvester S, Parkinson J, Mueller M, Yim J, Bailey DL, Back M, Drummond J. The MANGO study: a prospective investigation of oxygen enhanced and blood-oxygen level dependent MRI as imaging biomarkers of hypoxia in glioblastoma. Front Oncol 2023; 13:1306164. [PMID: 38192626 PMCID: PMC10773871 DOI: 10.3389/fonc.2023.1306164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 12/04/2023] [Indexed: 01/10/2024] Open
Abstract
Background Glioblastoma (GBM) is the most aggressive type of brain cancer, with a 5-year survival rate of ~5% and most tumours recurring locally within months of first-line treatment. Hypoxia is associated with worse clinical outcomes in GBM, as it leads to localized resistance to radiotherapy and subsequent tumour recurrence. Current standard of care treatment does not account for tumour hypoxia, due to the challenges of mapping tumour hypoxia in routine clinical practice. In this clinical study, we aim to investigate the role of oxygen enhanced (OE) and blood-oxygen level dependent (BOLD) MRI as non-invasive imaging biomarkers of hypoxia in GBM, and to evaluate their potential role in dose-painting radiotherapy planning and treatment response assessment. Methods The primary endpoint is to evaluate the quantitative and spatial correlation between OE and BOLD MRI measurements and [18F]MISO values of uptake in the tumour. The secondary endpoints are to evaluate the repeatability of MRI biomarkers of hypoxia in a test-retest study, to estimate the potential clinical benefits of using MRI biomarkers of hypoxia to guide dose-painting radiotherapy, and to evaluate the ability of MRI biomarkers of hypoxia to assess treatment response. Twenty newly diagnosed GBM patients will be enrolled in this study. Patients will undergo standard of care treatment while receiving additional OE/BOLD MRI and [18F]MISO PET scans at several timepoints during treatment. The ability of OE/BOLD MRI to map hypoxic tumour regions will be evaluated by assessing spatial and quantitative correlations with areas of hypoxic tumour identified via [18F]MISO PET imaging. Discussion MANGO (Magnetic resonance imaging of hypoxia for radiation treatment guidance in glioblastoma multiforme) is a diagnostic/prognostic study investigating the role of imaging biomarkers of hypoxia in GBM management. The study will generate a large amount of longitudinal multimodal MRI and PET imaging data that could be used to unveil dynamic changes in tumour physiology that currently limit treatment efficacy, thereby providing a means to develop more effective and personalised treatments.
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Affiliation(s)
- Caterina Brighi
- Image X Institute, Sydney School of Health Sciences, The University of Sydney, Sydney, NSW, Australia
| | - David E. J. Waddington
- Image X Institute, Sydney School of Health Sciences, The University of Sydney, Sydney, NSW, Australia
| | - Paul J. Keall
- Image X Institute, Sydney School of Health Sciences, The University of Sydney, Sydney, NSW, Australia
| | - Jeremy Booth
- Department of Radiation Oncology, Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, NSW, Australia
- Institute of Medical Physics, School of Physics, The University of Sydney, Sydney, NSW, Australia
| | | | - Shona Silvester
- Image X Institute, Sydney School of Health Sciences, The University of Sydney, Sydney, NSW, Australia
| | - Jonathon Parkinson
- Department of Neurosurgery, Royal North Shore Hospital, Sydney, NSW, Australia
- The Brain Cancer Group Sydney, St Leonards, NSW, Australia
| | - Marco Mueller
- Siemens Healthcare Pty Ltd, Brisbane, QLD, Australia
| | - Jackie Yim
- Department of Radiation Oncology, Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, NSW, Australia
- The Brain Cancer Group Sydney, St Leonards, NSW, Australia
- Centre for Health Economics Research and Evaluation, University of Technology Sydney, Sydney, NSW, Australia
| | - Dale L. Bailey
- Department of Nuclear Medicine, Royal North Shore Hospital, Sydney, NSW, Australia
| | - Michael Back
- Department of Radiation Oncology, Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, NSW, Australia
- The Brain Cancer Group Sydney, St Leonards, NSW, Australia
| | - James Drummond
- Department of Radiation Oncology, Northern Sydney Cancer Centre, Royal North Shore Hospital, Sydney, NSW, Australia
- The Brain Cancer Group Sydney, St Leonards, NSW, Australia
- Department of Neuroradiology, Royal North Shore Hospital, Sydney, NSW, Australia
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3
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Patel R, Provenzano D, Loew M. Anonymization and validation of three-dimensional volumetric renderings of computed tomography data using commercially available T1-weighted magnetic resonance imaging-based algorithms. J Med Imaging (Bellingham) 2023; 10:066501. [PMID: 38074629 PMCID: PMC10704182 DOI: 10.1117/1.jmi.10.6.066501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 11/03/2023] [Accepted: 11/07/2023] [Indexed: 02/12/2024] Open
Abstract
Purpose Previous studies have demonstrated that three-dimensional (3D) volumetric renderings of magnetic resonance imaging (MRI) brain data can be used to identify patients using facial recognition. We have shown that facial features can be identified on simulation-computed tomography (CT) images for radiation oncology and mapped to face images from a database. We aim to determine whether CT images can be anonymized using anonymization software that was designed for T1-weighted MRI data. Approach Our study examines (1) the ability of off-the-shelf anonymization algorithms to anonymize CT data and (2) the ability of facial recognition algorithms to identify whether faces could be detected from a database of facial images. Our study generated 3D renderings from 57 head CT scans from The Cancer Imaging Archive database. Data were anonymized using AFNI (deface, reface, and 3Dskullstrip) and FSL's BET. Anonymized data were compared to the original renderings and passed through facial recognition algorithms (VGG-Face, FaceNet, DLib, and SFace) using a facial database (labeled faces in the wild) to determine what matches could be found. Results Our study found that all modules were able to process CT data and that AFNI's 3Dskullstrip and FSL's BET data consistently showed lower reidentification rates compared to the original. Conclusions The results from this study highlight the potential usage of anonymization algorithms as a clinical standard for deidentifying brain CT data. Our study demonstrates the importance of continued vigilance for patient privacy in publicly shared datasets and the importance of continued evaluation of anonymization methods for CT data.
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Affiliation(s)
- Rahil Patel
- George Washington University School of Engineering and Applied Science, Department of Biomedical Engineering, Washington, District of Columbia, United States
| | - Destie Provenzano
- George Washington University School of Engineering and Applied Science, Department of Biomedical Engineering, Washington, District of Columbia, United States
| | - Murray Loew
- George Washington University School of Engineering and Applied Science, Department of Biomedical Engineering, Washington, District of Columbia, United States
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Perez RC, Kim D, Maxwell AWP, Camacho JC. Functional Imaging of Hypoxia: PET and MRI. Cancers (Basel) 2023; 15:3336. [PMID: 37444446 DOI: 10.3390/cancers15133336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 06/22/2023] [Accepted: 06/22/2023] [Indexed: 07/15/2023] Open
Abstract
Molecular and functional imaging have critical roles in cancer care. Existing evidence suggests that noninvasive detection of hypoxia within a particular type of cancer can provide new information regarding the relationship between hypoxia, cancer aggressiveness and altered therapeutic responses. Following the identification of hypoxia inducible factor (HIF), significant progress in understanding the regulation of hypoxia-induced genes has been made. These advances have provided the ability to therapeutically target HIF and tumor-associated hypoxia. Therefore, by utilizing the molecular basis of hypoxia, hypoxia-based theranostic strategies are in the process of being developed which will further personalize care for cancer patients. The aim of this review is to provide an overview of the significance of tumor hypoxia and its relevance in cancer management as well as to lay out the role of imaging in detecting hypoxia within the context of cancer.
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Affiliation(s)
- Ryan C Perez
- Florida State University College of Medicine, Tallahassee, FL 32306, USA
| | - DaeHee Kim
- Department of Diagnostic Imaging, The Warren Alpert Medical School, Brown University, Providence, RI 02903, USA
| | - Aaron W P Maxwell
- Department of Diagnostic Imaging, The Warren Alpert Medical School, Brown University, Providence, RI 02903, USA
| | - Juan C Camacho
- Department of Clinical Sciences, Florida State University College of Medicine, Tallahassee, FL 32306, USA
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Calabrese E, Villanueva-Meyer JE, Rudie JD, Rauschecker AM, Baid U, Bakas S, Cha S, Mongan JT, Hess CP. The University of California San Francisco Preoperative Diffuse Glioma MRI Dataset. Radiol Artif Intell 2022; 4:e220058. [PMID: 36523646 PMCID: PMC9748624 DOI: 10.1148/ryai.220058] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 07/05/2022] [Accepted: 08/02/2022] [Indexed: 06/10/2023]
Abstract
Supplemental material is available for this article. Keywords: Informatics, MR Diffusion Tensor Imaging, MR Perfusion, MR Imaging, Neuro-Oncology, CNS, Brain/Brain Stem, Oncology, Radiogenomics, Radiology-Pathology Integration © RSNA, 2022.
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Affiliation(s)
- Evan Calabrese
- From the Center for Intelligent Imaging (Ci), Department
of Radiology & Biomedical Imaging, University of California San
Francisco, 505 Parnassus Ave, San Francisco, CA 94143 (E.C., J.E.V.M., J.D.R.,
A.M.R., S.C., J.T.M., C.P.H.); and Center for Biomedical Image Computing and
Analytics (CBICA), University of Pennsylvania, Philadelphia, Pa (U.B.,
S.B.)
| | - Javier E. Villanueva-Meyer
- From the Center for Intelligent Imaging (Ci), Department
of Radiology & Biomedical Imaging, University of California San
Francisco, 505 Parnassus Ave, San Francisco, CA 94143 (E.C., J.E.V.M., J.D.R.,
A.M.R., S.C., J.T.M., C.P.H.); and Center for Biomedical Image Computing and
Analytics (CBICA), University of Pennsylvania, Philadelphia, Pa (U.B.,
S.B.)
| | - Jeffrey D. Rudie
- From the Center for Intelligent Imaging (Ci), Department
of Radiology & Biomedical Imaging, University of California San
Francisco, 505 Parnassus Ave, San Francisco, CA 94143 (E.C., J.E.V.M., J.D.R.,
A.M.R., S.C., J.T.M., C.P.H.); and Center for Biomedical Image Computing and
Analytics (CBICA), University of Pennsylvania, Philadelphia, Pa (U.B.,
S.B.)
| | - Andreas M. Rauschecker
- From the Center for Intelligent Imaging (Ci), Department
of Radiology & Biomedical Imaging, University of California San
Francisco, 505 Parnassus Ave, San Francisco, CA 94143 (E.C., J.E.V.M., J.D.R.,
A.M.R., S.C., J.T.M., C.P.H.); and Center for Biomedical Image Computing and
Analytics (CBICA), University of Pennsylvania, Philadelphia, Pa (U.B.,
S.B.)
| | - Ujjwal Baid
- From the Center for Intelligent Imaging (Ci), Department
of Radiology & Biomedical Imaging, University of California San
Francisco, 505 Parnassus Ave, San Francisco, CA 94143 (E.C., J.E.V.M., J.D.R.,
A.M.R., S.C., J.T.M., C.P.H.); and Center for Biomedical Image Computing and
Analytics (CBICA), University of Pennsylvania, Philadelphia, Pa (U.B.,
S.B.)
| | - Spyridon Bakas
- From the Center for Intelligent Imaging (Ci), Department
of Radiology & Biomedical Imaging, University of California San
Francisco, 505 Parnassus Ave, San Francisco, CA 94143 (E.C., J.E.V.M., J.D.R.,
A.M.R., S.C., J.T.M., C.P.H.); and Center for Biomedical Image Computing and
Analytics (CBICA), University of Pennsylvania, Philadelphia, Pa (U.B.,
S.B.)
| | - Soonmee Cha
- From the Center for Intelligent Imaging (Ci), Department
of Radiology & Biomedical Imaging, University of California San
Francisco, 505 Parnassus Ave, San Francisco, CA 94143 (E.C., J.E.V.M., J.D.R.,
A.M.R., S.C., J.T.M., C.P.H.); and Center for Biomedical Image Computing and
Analytics (CBICA), University of Pennsylvania, Philadelphia, Pa (U.B.,
S.B.)
| | - John T. Mongan
- From the Center for Intelligent Imaging (Ci), Department
of Radiology & Biomedical Imaging, University of California San
Francisco, 505 Parnassus Ave, San Francisco, CA 94143 (E.C., J.E.V.M., J.D.R.,
A.M.R., S.C., J.T.M., C.P.H.); and Center for Biomedical Image Computing and
Analytics (CBICA), University of Pennsylvania, Philadelphia, Pa (U.B.,
S.B.)
| | - Christopher P. Hess
- From the Center for Intelligent Imaging (Ci), Department
of Radiology & Biomedical Imaging, University of California San
Francisco, 505 Parnassus Ave, San Francisco, CA 94143 (E.C., J.E.V.M., J.D.R.,
A.M.R., S.C., J.T.M., C.P.H.); and Center for Biomedical Image Computing and
Analytics (CBICA), University of Pennsylvania, Philadelphia, Pa (U.B.,
S.B.)
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6
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Bakas S, Sako C, Akbari H, Bilello M, Sotiras A, Shukla G, Rudie JD, Santamaría NF, Kazerooni AF, Pati S, Rathore S, Mamourian E, Ha SM, Parker W, Doshi J, Baid U, Bergman M, Binder ZA, Verma R, Lustig RA, Desai AS, Bagley SJ, Mourelatos Z, Morrissette J, Watt CD, Brem S, Wolf RL, Melhem ER, Nasrallah MP, Mohan S, O'Rourke DM, Davatzikos C. The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics. Sci Data 2022; 9:453. [PMID: 35906241 PMCID: PMC9338035 DOI: 10.1038/s41597-022-01560-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 07/12/2022] [Indexed: 02/05/2023] Open
Abstract
Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the "University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics" (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.
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Affiliation(s)
- Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hamed Akbari
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michel Bilello
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Aristeidis Sotiras
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology and Institute for Informatics, Washington University, School of Medicine, St. Louis, MO, USA
| | - Gaurav Shukla
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiation Oncology, Christiana Care Health System, Philadelphia, PA, USA
| | - Jeffrey D Rudie
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Natali Flores Santamaría
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anahita Fathi Kazerooni
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarthak Pati
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Saima Rathore
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sung Min Ha
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology and Institute for Informatics, Washington University, School of Medicine, St. Louis, MO, USA
| | - William Parker
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mark Bergman
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | - Zev A Binder
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ragini Verma
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Robert A Lustig
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arati S Desai
- Division of Hematology Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Stephen J Bagley
- Division of Hematology Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Zissimos Mourelatos
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jennifer Morrissette
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher D Watt
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Steven Brem
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ronald L Wolf
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elias R Melhem
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - MacLean P Nasrallah
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Suyash Mohan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Donald M O'Rourke
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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7
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Barajas RF, Ambady P, Link J, Krohn KA, Raslan A, Mallak N, Woltjer R, Muldoon L, Neuwelt EA. [ 18F]-fluoromisonidazole (FMISO) PET/MRI hypoxic fraction distinguishes neuroinflammatory pseudoprogression from recurrent glioblastoma in patients treated with pembrolizumab. Neurooncol Pract 2022; 9:246-250. [PMID: 35601969 PMCID: PMC9113243 DOI: 10.1093/nop/npac021] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Response assessment after immunotherapy remains a major challenge in glioblastoma due to an expected increased incidence of pseudoprogression. Gadolinium-enhanced magnetic resonance imaging (MRI) is the standard for monitoring therapeutic response, however, is markedly limited in characterizing pseudoprogression. Given that hypoxia is an important defining feature of glioblastoma regrowth, we hypothesized that [18F]-fluoromisonidazole (FMISO) positron emission tomography (PET) could provide an additional physiological measure for the diagnosis of immunotherapeutic failure. Six patients with newly diagnosed glioblastoma who had previously received maximal safe resection followed by Stupp protocol CRT concurrent with pembrolizumab immunotherapy were recruited for FMISO PET and Gd-MRI at the time of presumed progression. The hypoxic fraction was defined as the ratio of hypoxic volume to T1-weighted gadolinium-enhancing volume. Four patients diagnosed with pseudoprogression demonstrated a mean hypoxic fraction of 9.8 ± 10%. Two with recurrent tumor demonstrated a mean hypoxic fraction of 131 ± 66%. Our results, supported by histopathology, suggest that the noninvasive assessment of hypoxic fraction by FMISO PET/MRI is clinically feasible and may serve as a biologically specific metric of therapeutic failure.
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Affiliation(s)
- Ramon F Barajas
- Department of Radiology, Neuroradiology Section, Oregon Health & Science University, Portland Oregon, USA
- Knight Cancer Institute Translational Oncology Program, Oregon Health & Science University, Portland, Oregon, USA
| | - Prakash Ambady
- Neuro-Oncology and Blood-Brain Barrier Program, Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Jeanne Link
- Center for Radiochemistry Research, Oregon Health & Science University, Portland, Oregon, USA
| | - Kenneth A Krohn
- Center for Radiochemistry Research, Oregon Health & Science University, Portland, Oregon, USA
| | - Ahmed Raslan
- Department of Neurological Surgery, Oregon Health & Science University, Portland, Oregon, USA
| | - Nadine Mallak
- Advanced Imaging Research Center, Oregon Health & Science University, Portland Oregon, USA
| | - Randy Woltjer
- Department of Pathology, Oregon Health & Science University, Portland, Oregon, USA
| | - Leslie Muldoon
- Neuro-Oncology and Blood-Brain Barrier Program, Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
| | - Edward A Neuwelt
- Neuro-Oncology and Blood-Brain Barrier Program, Department of Neurology, Oregon Health & Science University, Portland, Oregon, USA
- Department of Neurological Surgery, Oregon Health & Science University, Portland, Oregon, USA
- Office of Research and Development, Portland Veterans Affairs Medical Center, Portland, Oregon, USA
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8
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Li M, Zhang Q, Yang K. Role of MRI-Based Functional Imaging in Improving the Therapeutic Index of Radiotherapy in Cancer Treatment. Front Oncol 2021; 11:645177. [PMID: 34513659 PMCID: PMC8429950 DOI: 10.3389/fonc.2021.645177] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 07/30/2021] [Indexed: 02/05/2023] Open
Abstract
Advances in radiation technology, such as intensity-modulated radiation therapy (IMRT), have largely enabled a biological dose escalation of the target volume (TV) and reduce the dose to adjacent tissues or organs at risk (OARs). However, the risk of radiation-induced injury increases as more radiation dose utilized during radiation therapy (RT), which predominantly limits further increases in TV dose distribution and reduces the local control rate. Thus, the accurate target delineation is crucial. Recently, technological improvements for precise target delineation have obtained more attention in the field of RT. The addition of functional imaging to RT can provide a more accurate anatomy of the tumor and normal tissues (such as location and size), along with biological information that aids to optimize the therapeutic index (TI) of RT. In this review, we discuss the application of some common MRI-based functional imaging techniques in clinical practice. In addition, we summarize the main challenges and prospects of these imaging technologies, expecting more inspiring developments and more productive research paths in the near future.
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Affiliation(s)
- Mei Li
- Department of Gynecology and Obstetrics, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China.,West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Qin Zhang
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Kaixuan Yang
- Department of Gynecology and Obstetrics, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China
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9
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Talati P, El-Abtah M, Kim D, Dietrich J, Fu M, Wenke M, He J, Natheir SN, Vangel M, Rapalino O, Vaynrub A, Arrillaga-Romany I, Forst DA, Yen YF, Andronesi O, Kalpathy-Cramer J, Rosen B, Batchelor TT, Gonzalez RG, Gerstner ER, Ratai EM. MR spectroscopic imaging predicts early response to anti-angiogenic therapy in recurrent glioblastoma. Neurooncol Adv 2021; 3:vdab060. [PMID: 34131648 PMCID: PMC8193903 DOI: 10.1093/noajnl/vdab060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Background Determining failure to anti-angiogenic therapy in recurrent glioblastoma (GBM) (rGBM) remains a challenge. The purpose of the study was to assess treatment response to bevacizumab-based therapy in patients with rGBM using MR spectroscopy (MRS). Methods We performed longitudinal MRI/MRS in 33 patients with rGBM to investigate whether changes in N-acetylaspartate (NAA)/Choline (Cho) and Lactate (Lac)/NAA from baseline to subsequent time points after treatment can predict early failures to bevacizumab-based therapies. Results After stratifying based on 9-month survival, longer-term survivors had increased NAA/Cho and decreased Lac/NAA levels compared to shorter-term survivors. ROC analyses for intratumoral NAA/Cho correlated with survival at 1 day, 2 weeks, 8 weeks, and 16 weeks. Intratumoral Lac/NAA ROC analyses were predictive of survival at all time points tested. At the 8-week time point, 88% of patients with decreased NAA/Cho did not survive 9 months; furthermore, 90% of individuals with an increased Lac/NAA from baseline did not survive at 9 months. No other metabolic ratios tested significantly predicted survival. Conclusions Changes in metabolic levels of tumoral NAA/Cho and Lac/NAA can serve as early biomarkers for predicting treatment failure to anti-angiogenic therapy as soon as 1 day after bevacizumab-based therapy. The addition of MRS to conventional MR methods can provide better insight into how anti-angiogenic therapy affects tumor microenvironment and predict patient outcomes.
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Affiliation(s)
- Pratik Talati
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Mohamed El-Abtah
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Daniel Kim
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jorg Dietrich
- Harvard Medical School, Boston, Massachusetts, USA.,Massachusetts General Hospital, Cancer Center, Boston, Massachusetts, USA
| | - Melanie Fu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Michael Wenke
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Julian He
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Sharif N Natheir
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Mark Vangel
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Otto Rapalino
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Anna Vaynrub
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Isabel Arrillaga-Romany
- Harvard Medical School, Boston, Massachusetts, USA.,Massachusetts General Hospital, Cancer Center, Boston, Massachusetts, USA
| | - Deborah A Forst
- Massachusetts General Hospital, Cancer Center, Boston, Massachusetts, USA
| | - Yi-Fen Yen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Ovidiu Andronesi
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Bruce Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Tracy T Batchelor
- Harvard Medical School, Boston, Massachusetts, USA.,Massachusetts General Hospital, Cancer Center, Boston, Massachusetts, USA
| | - R Gilberto Gonzalez
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Elizabeth R Gerstner
- Harvard Medical School, Boston, Massachusetts, USA.,Massachusetts General Hospital, Cancer Center, Boston, Massachusetts, USA
| | - Eva-Maria Ratai
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
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10
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Dalvit Carvalho da Silva R, Jenkyn TR, Carranza VA. Development of a Convolutional Neural Network Based Skull Segmentation in MRI Using Standard Tesselation Language Models. J Pers Med 2021; 11:310. [PMID: 33923480 PMCID: PMC8074044 DOI: 10.3390/jpm11040310] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 03/26/2021] [Accepted: 04/15/2021] [Indexed: 11/16/2022] Open
Abstract
Segmentation is crucial in medical imaging analysis to help extract regions of interest (ROI) from different imaging modalities. The aim of this study is to develop and train a 3D convolutional neural network (CNN) for skull segmentation in magnetic resonance imaging (MRI). 58 gold standard volumetric labels were created from computed tomography (CT) scans in standard tessellation language (STL) models. These STL models were converted into matrices and overlapped on the 58 corresponding MR images to create the MRI gold standards labels. The CNN was trained with these 58 MR images and a mean ± standard deviation (SD) Dice similarity coefficient (DSC) of 0.7300 ± 0.04 was achieved. A further investigation was carried out where the brain region was removed from the image with the help of a 3D CNN and manual corrections by using only MR images. This new dataset, without the brain, was presented to the previous CNN which reached a new mean ± SD DSC of 0.7826 ± 0.03. This paper aims to provide a framework for segmenting the skull using CNN and STL models, as the 3D CNN was able to segment the skull with a certain precision.
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Affiliation(s)
- Rodrigo Dalvit Carvalho da Silva
- Craniofacial Injury and Concussion Research Laboratory, Western University, London, ON N6A 3K7, Canada; (T.R.J.); (V.A.C.)
- Faculty of Engineering, School of Biomedical Engineering, Western University, London, ON N6A 3K7, Canada
| | - Thomas Richard Jenkyn
- Craniofacial Injury and Concussion Research Laboratory, Western University, London, ON N6A 3K7, Canada; (T.R.J.); (V.A.C.)
- Faculty of Engineering, School of Biomedical Engineering, Western University, London, ON N6A 3K7, Canada
- Department of Mechanical and Materials Engineering, Western University, London, ON N6A 3K7, Canada
- Faculty of Health Sciences, School of Kinesiology, Western University, London, ON N6A 3K7, Canada
- Wolf Orthopaedic Biomechanics Laboratory, Fowler Kennedy Sport Medicine Clinic, London, ON N6A 3K7, Canada
| | - Victor Alexander Carranza
- Craniofacial Injury and Concussion Research Laboratory, Western University, London, ON N6A 3K7, Canada; (T.R.J.); (V.A.C.)
- Faculty of Engineering, School of Biomedical Engineering, Collaborative Specialization in Musculoskeletal Health Research, and Bone and Joint Institute, Western University, London, ON N6A 3K7, Canada
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11
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Dalvit Carvalho da Silva R, Jenkyn TR, Carranza VA. Convolutional Neural Networks and Geometric Moments to Identify the Bilateral Symmetric Midplane in Facial Skeletons from CT Scans. BIOLOGY 2021; 10:biology10030182. [PMID: 33801432 PMCID: PMC7999007 DOI: 10.3390/biology10030182] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 02/11/2021] [Accepted: 02/25/2021] [Indexed: 12/15/2022]
Abstract
Simple Summary The bilateral symmetry midplane of the facial skeleton plays a critical role in reconstructive craniofacial surgery. By accurately locating the midplane, surgeons can use the undeformed side of the face as a template for the malformed side. However, the location of the midline is still a subjective procedure, despite its importance. This study aimed to present a 3D technique for automatically calculating the craniofacial symmetry midline of the facial skeleton from CT scans using deep learning techniques. A total of 195 skull images were evaluated and were found to be reliable and provided good accuracy in symmetric images. Abstract In reconstructive craniofacial surgery, the bilateral symmetry of the midplane of the facial skeleton plays an important role in surgical planning. Surgeons can take advantage of the intact side of the face as a template for the malformed side by accurately locating the midplane to assist in the preparation of the surgical procedure. However, despite its importance, the location of the midline is still a subjective procedure. The aim of this study was to present a 3D technique using a convolutional neural network and geometric moments to automatically calculate the craniofacial midline symmetry of the facial skeleton from CT scans. To perform this task, a total of 195 skull images were assessed to validate the proposed technique. In the symmetry planes, the technique was found to be reliable and provided good accuracy. However, further investigations to improve the results of asymmetric images may be carried out.
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Affiliation(s)
- Rodrigo Dalvit Carvalho da Silva
- Craniofacial Injury and Concussion Research Laboratory, Western University, London, ON N6A 3K7, Canada; (T.R.J.); (V.A.C.)
- School of Biomedical Engineering, Faculty of Engineering, Western University, London, ON N6A 3K7, Canada
- Correspondence:
| | - Thomas Richard Jenkyn
- Craniofacial Injury and Concussion Research Laboratory, Western University, London, ON N6A 3K7, Canada; (T.R.J.); (V.A.C.)
- School of Biomedical Engineering, Faculty of Engineering, Western University, London, ON N6A 3K7, Canada
- Department of Mechanical and Materials Engineering, Western University, London, ON N6A 3K7, Canada
- School of Kinesiology, Faculty of Health Sciences, Western University, London, ON N6A 3K7, Canada
- Wolf Orthopaedic Biomechanics Laboratory, Fowler Kennedy Sport Medicine Clinic, London, ON N6A 3K7, Canada
| | - Victor Alexander Carranza
- Craniofacial Injury and Concussion Research Laboratory, Western University, London, ON N6A 3K7, Canada; (T.R.J.); (V.A.C.)
- School of Biomedical Engineering, Faculty of Engineering, Collaborative Specialization in Musculoskeletal Health Research, and Bone and Joint Institute, Western University, London, ON N6A 3K7, Canada
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12
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Menze B, Isensee F, Wiest R, Wiestler B, Maier-Hein K, Reyes M, Bakas S. Analyzing magnetic resonance imaging data from glioma patients using deep learning. Comput Med Imaging Graph 2021; 88:101828. [PMID: 33571780 PMCID: PMC8040671 DOI: 10.1016/j.compmedimag.2020.101828] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 10/29/2020] [Accepted: 11/18/2020] [Indexed: 12/21/2022]
Abstract
The quantitative analysis of images acquired in the diagnosis and treatment of patients with brain tumors has seen a significant rise in the clinical use of computational tools. The underlying technology to the vast majority of these tools are machine learning methods and, in particular, deep learning algorithms. This review offers clinical background information of key diagnostic biomarkers in the diagnosis of glioma, the most common primary brain tumor. It offers an overview of publicly available resources and datasets for developing new computational tools and image biomarkers, with emphasis on those related to the Multimodal Brain Tumor Segmentation (BraTS) Challenge. We further offer an overview of the state-of-the-art methods in glioma image segmentation, again with an emphasis on publicly available tools and deep learning algorithms that emerged in the context of the BraTS challenge.
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Affiliation(s)
- Bjoern Menze
- Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
| | | | - Roland Wiest
- Support Center for Advanced Neuroimaging, Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern, Switzerland.
| | | | | | | | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
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13
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Intratumoral Distribution of Lactate and the Monocarboxylate Transporters 1 and 4 in Human Glioblastoma Multiforme and Their Relationships to Tumor Progression-Associated Markers. Int J Mol Sci 2020; 21:ijms21176254. [PMID: 32872409 PMCID: PMC7504270 DOI: 10.3390/ijms21176254] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 08/27/2020] [Indexed: 12/14/2022] Open
Abstract
(1) Background: Metabolic reprogramming has been postulated to be one of the hallmarks of cancer, thus representing a promising therapeutic target also in glioblastoma multiforme (GBM). Hypoxic tumor cells produce lactate, and monocarboxylate transporters (MCTs) play an important role in its distribution; (2) Methods: We examined the distribution of lactate by multi voxel magnetic resonance spectroscopic imaging and ELISA in glioblastoma multiforme (GBM) patients. In addition, we investigated the expression and cellular localization of MCT1, MCT4, and of several markers connected to tumor progression by quantitative PCR and immunofluorescence double-staining in human GBM ex vivo tissues; (3) Results: The highest lactate concentration was found at the center of the vital parts of the tumor. Three main GBM groups could be distinguished according to their regional gene expression differences of the investigated genes. MCT1 and MCT4 were found on cells undergoing epithelial to mesenchymal transition and on tumor stem-like cells. GBM cells revealing an expression of cellular dormancy markers, showed positive staining for MCT4; (4) Conclusion: Our findings indicate the existence of individual differences in the regional distribution of MCT1 and MCT4 and suggest that both transporters have distinct connections to GBM progression processes, which could contribute to the drug resistance of MCT-inhibitors.
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14
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Riccardo L, Natale Q, Pierpaolo A, Domenico A, Maria G, Rexhep D, Francesco B, Sergio B. 18F-FMISO PET imaging: insights over MRI in patients with glioma. Clin Transl Imaging 2020. [DOI: 10.1007/s40336-019-00353-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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15
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Analysis of Glioblastoma Multiforme Tumor Metabolites Using Multivoxel Magnetic Resonance Spectroscopy. Avicenna J Med Biotechnol 2020; 12:107-115. [PMID: 32431795 PMCID: PMC7229458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Glioblastoma Multiforme (GBM) is the most common and deadly type of primary brain tumor in adults. Magnetic Resonance Spectroscopy (MRS) is a noninvasive imaging technique used to study metabolic changes in the brain tumors. Some metabolites such as Phosphocholine, Creatine, NAA/Cr, and Pcho/Cr have been proven to show a diagnostic role in GBM. The present study was conducted to analyze important metabolites using MRS multivoxel in GBM tumor. METHODS In this study, information was collected from 8 individuals diagnosed with GBM using Siemens multivoxel MRS with a magnetic field strength of 3 T. Data were obtained by Point-Resolved Spectroscopy (PRESS) protocol with TE=135 ms and TR=1570 ms. NAA, Pcho, Cr, Ala, Gln, Gly, Glu, Lac, NAAG, and Tau metabolites were extracted and evaluated statistically. RESULTS Given total number of normal voxels and total number of all voxels, levels of Cr, Glu, NAA, NAAG, and Gly/Tau ratio in healthy voxels were significantly higher than tumoral voxels (p=0.005, p=0.03, p<0.001, p<0.001 and p=0.041, respectively). In contrast, levels of Gly, Gln, Tau, Lac/Cr, Pcho/Cr, Pcho/NAA, Lac/NAA, and Gln/Glu ratios in tumoral voxels were significantly more than healthy voxels (p=0.001, p= 0.037, p<0.001, p=0.010, p<0.001, p<0.001, and p=0.024, respectively). However, levels of Lac and Pcho had no significant difference in the two types of voxels. CONCLUSION In summary, compared to patients with glioblastoma with 1H-MRS, the Pcho/Cr and Pcho/NAA ratios, and NAAG are the most important parameters to differentiate between tumoral and normal voxels.
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16
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Julià-Sapé M, Candiota AP, Arús C. Cancer metabolism in a snapshot: MRS(I). NMR IN BIOMEDICINE 2019; 32:e4054. [PMID: 30633389 DOI: 10.1002/nbm.4054] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Revised: 11/02/2018] [Accepted: 11/05/2018] [Indexed: 06/09/2023]
Abstract
The contribution of MRS(I) to the in vivo evaluation of cancer-metabolism-derived metrics, mostly since 2016, is reviewed here. Increased carbon consumption by tumour cells, which are highly glycolytic, is now being sampled by 13 C magnetic resonance spectroscopic imaging (MRSI) following the injection of hyperpolarized [1-13 C] pyruvate (Pyr). Hot-spots of, mostly, increased lactate dehydrogenase activity or flow between Pyr and lactate (Lac) have been seen with cancer progression in prostate (preclinical and in humans), brain and pancreas (both preclinical) tumours. Therapy response is usually signalled by decreased Lac/Pyr 13 C-labelled ratio with respect to untreated or non-responding tumour. For therapeutic agents inducing tumour hypoxia, the 13 C-labelled Lac/bicarbonate ratio may be a better metric than the Lac/Pyr ratio. 31 P MRSI may sample intracellular pH changes from brain tumours (acidification upon antiangiogenic treatment, basification at fast proliferation and relapse). The steady state tumour metabolome pattern is still in use for cancer evaluation. Metrics used for this range from quantification of single oncometabolites (such as 2-hydroxyglutarate in mutant IDH1 glial brain tumours) to selected metabolite ratios (such as total choline to N-acetylaspartate (plain ratio or CNI index)) or the whole 1 H MRSI(I) pattern through pattern recognition analysis. These approaches have been applied to address different questions such as tumour subtype definition, following/predicting the response to therapy or defining better resection or radiosurgery limits.
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Affiliation(s)
- Margarida Julià-Sapé
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain
- Departament de Bioquímica i Biologia Molecular, Unitat de Bioquímica de Biociències, Universitat Autònoma de Barcelona (UAB), Cerdanyola del Vallès, Spain
- Institut de Biotecnologia i de Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), Cerdanyola del Vallès, Spain
| | - Ana Paula Candiota
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain
- Departament de Bioquímica i Biologia Molecular, Unitat de Bioquímica de Biociències, Universitat Autònoma de Barcelona (UAB), Cerdanyola del Vallès, Spain
- Institut de Biotecnologia i de Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), Cerdanyola del Vallès, Spain
| | - Carles Arús
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain
- Departament de Bioquímica i Biologia Molecular, Unitat de Bioquímica de Biociències, Universitat Autònoma de Barcelona (UAB), Cerdanyola del Vallès, Spain
- Institut de Biotecnologia i de Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), Cerdanyola del Vallès, Spain
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